Sentiment Analysis "The bag-of-words model we developed in a classifier for Sentiment Analysis of movie reviews will now be put to use. By the end of this, you ought to be able to define sentimentanalysis, train a classifier for sentiment analysis using nltk, and assess the model's accuracyusing training and test data." Would you like to know more about sentiment analysis? In that case, you've found the right location. The process of locating attitudes or feelings hidden within a body of text is knownas sentiment analysis. It is frequently used to examine consumer reviews and feedback onproducts. In this article, we will show you how to train a Sentiment Analysis classifier using the movie review corpus. We will be using the Naive Bayes Classifier from nltk, which is a simpleclassifier with a probabilistic approach to classification. Let's take a step back and go over some of the fundamentals before getting into the specifics. Classification calls for labels from ground truth data and is a supervised activity. Inour scenario, the bag-of-words model we previously made will be combined with thecollected negative and positive reviews we downloaded. We can assign a positive ornegative label to each review bag-of-words using the bag-of-words model. Switching to our notebook now, let's get to work assembling the input and label datasets for the classifier. As the database has been vetted to differentiate positive and negativeevaluations, we are fortunate to be employing the Naive Bayes classifier. In order to createtwo dictionaries as a "bag of words" for positive and bad assessments, we will utilize this asthe basis for our research. We have a negative features, build bag-of-words features filtered, and we'll have a label "neg" associated with that. For each file in negative fields, we'll build a bag-of-words andstore that in negative features. We can do the same thing for positive features. Now we haveour two features, and we remember we have 1,000 records in them. We can use 80% of the data for classification in Naive Bayes. When we provide the first 800 rows in each feature, it's 80%, so we'll store that number, 800, in a variable called "split".We'll use that split to slice the first 800 for training and the remaining 200 for testing later on. Now let's construct a classifier using the Naive Bayes Classifier. We import it from nltk classify and give it the first 800 rows of data for training from each feature. Now that we havethe last 200 rows from each feature, we can evaluate the correctness of our model. The accuracy of our model is determined by the number of correct predictions divided by the total number of predictions. We can see that our model has an accuracy of 74.5% on thetest data.
Using the movie review corpus and the Naive Bayes Classifier from nltk, we have demonstrated how to train a Sentiment Analysis classifier. The bag-of-words methodologywe previously developed allowed us to categorize each review as either favorable ornegative. After that, we trained our model with the Naive Bayes Classifier, which had a74.5% accuracy rate on the test data. If you are interested in Sentiment Analysis, we recommend you to explore further and experiment with different models and datasets. The possibilities are endless, and with theright tools and knowledge, you can gain valuable insights from customer feedback andproduct reviews.